Targeting amine- and phenol-containing metabolites in urine by dansylation isotope labeling and liquid chromatography mass spectrometry for evaluation of bladder cancer biomarkers
Bibliographic record
Abstract
Metabolomics is considered an effective approach for understanding metabolic responses in complex biological systems. Accordingly, it has attracted increasing attention for biomarker discovery, especially in cancer. In this study, we used a non-invasive method to evaluate four urine metabolite biomarker candidates—o-phosphoethanolamine, 3-amio-2-piperidone, uridine and 5-hydroxyindoleactic acid—for their potential as bladder cancer diagnostic biomarkers. To analyze these targeted amine- and phenol-containing metabolites, we used differential 12 C 2 -/ 13 C 2 -dansylation labeling coupled with liquid chromatography/tandem mass spectrometry, which has previously been demonstrated to exhibit high sensitivity and reproducibility. Specifically, we used ultra-performance liquid chromatography (UPLC) coupled with high-resolution Fourier transform ion-cyclotron resonance MS system (LC-FT/MS) and an ion trap MS with MRM function (LC-HCT/MS) for targeted quantification. The urinary metabolites of interest were well separated and quantified using this approach. To apply this approach to clinical urine specimens, we spiked samples with 13 C 2 -dansylatedsynthetic compounds, which served as standards for targeted quantification of 12 C 2 -dansylated urinary endogenous metabolites using LC-FT/MS as well as LC-HCT/MS with MRM mode. These analyses revealed significant differences in two of the four metabolites of interest—o-phosphoethanolamine and uridine—between bladder cancer and non-cancer groups. O-phosphoethanolamine was the most promising single biomarker, with an area-under-the-curve (AUC) value of 0.709 for bladder cancer diagnosis. Diagnostic performance was improved by combining uridine and o-phosphoethanolamine in a marker panel, yielding an AUC value of 0.726. This study confirmed discovery-phase features of the urine metabolome of bladder cancer patients and verified their importance for further study.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".